• Journal of Global Scholars of Marketing Science
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• v.12
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• pp.121-134
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• 2003

Weather marketing is firms' effort to incorporate changes of diverse weather factors into marketing planning and activities. The concept has already been applied in many products with mostly seasonal variation. However researches in this area have been limited only in practical areas and has not been supported by scientifIc approaches. Here, we investigated the effect of diverse weather factors like temperature, rain and wind on product sales based on empirical data and scientifIc methodology. For this, we selected the fashion clothing items in department stores. We tried to fInd the relationship between daily sales of clothing items and daily whether factors. Results showed that there is a meaningful relation between the two factors.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.16 no.6
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• pp.63-75
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• 2013

The purpose of the study is investigating the price fluctuation of landscaping plants in the Information on Commodity Prices(ICP) and the posted price fluctuation of landscaping plants of Public Procurement Service(PPS) recent 10 years. It also provides the basic information which can be applied to production and sales of landscaping plants, comparing with general price index. The major findings of the study are as follows. First, The price of investigated plants of PPS has increased about 4.56% in average recent 10 years. Among this increase, of evergreen tree was predominant. On the other hand, landscaping trees price of ICP has increased about only 2.34% in average. Secondly, The result shows that average price of investigated plants of PPS is positively related with the price of ICP. For this reason, we found that prices of ICP and of PPS move together in most case. However, we found that there are no relation between Consumer Price Index(CPI), Producer Price Index(PPI) and Agricultural Price Index(API). Therefore, price fluctuation of landscaping trees moves regardless of normal price fluctuation in general. Third, even though result shows that price index of evergreen trees, deciduous trees and shrubs are weakly related with normal price index partly, it was not high enough to be significant. According to the result, we found that price of landscaping plants is not related with market situation. For this reason, we thought that there are some difficulties for the reasonable production and sales of landscaping plants because the price is somewhat decided by rule of thumb. Therefore, understanding the composition of cost and making prediction by price fluctuation available are needed so that it can be practically conducive to reasonable production and sales.

• Journal of Korean Society for Quality Management
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• v.48 no.1
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• pp.171-186
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• 2020

Purpose: The purpose of this study is conducting of predictive models that considered multicollinearity of independent variables in order to carry out more efficient and reliable predictions about differential pressure in seawater reverse osmosis. Methods: The main variables of each RO system are extracted through factor analysis. Common variables are derived through comparison of RO system # 1 and RO system # 2. In order to carry out the prediction modeling about the differential pressure, which is the target variable, we constructed the prediction model reflecting the regression analysis, the artificial neural network, and the support vector machine in R package, and figured out the superiority of the model by comparing RMSE. Results: The number of factors extracted from factor analysis of RO system #1 and RO system #2 is same. And the value of variability(% Var) increased as step proceeds according to the analysis procedure. As a result of deriving the average RMSE of the models, the overall prediction of the SVM was superior to the other models. Conclusion: This study is meaningful in that it has been conducting a demonstration study of considering the multicollinearity of independent variables. Before establishing a predictive model for a target variable, it would be more accurate predictive model if the relevant variables are derived and reflected.

• Journal of Distribution Science
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• v.4 no.2
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• pp.5-20
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• 2006

Retail is called location business because it is one of the most important factors to estimate management of stores for retailers who are going to sell products directly to customers. Retailers' management achievements are shown in sale in general. Therefore, retailers tend to focus on ways to increase the numbers of customers in order to raise sales. First of all, in this research, I am going to examine the most fundamental models such as Reilly's retail gravitation, converse model, huff probability model and multiful losit model in selecting stores. Secondly, I am going to provide the process and analyzing ways to predict estimated sales amount with the previous theory model. Also I am going to predict estimated sales amount of the department store L which is located in D metorpolitan city. Lastly, I am going to argue about the problem of this research and the next research subject. Our main goal is to provide ways to complement and inspect sales estimation models, which can be used in fields after taking characters of high class structure of Korea into consideration on the base of previous researches. According to the result of the research, my conclusion is that if the process of analysis and changing factors are complemented, revise model, which can reflect reality of Korea, will be provided. Therefore, in the future study, we have to build up theory models to suit for our retail market through critic reviews about the existing high class structure of Korea.

• Journal of Intelligence and Information Systems
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• v.21 no.3
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• pp.79-99
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• 2015

The prediction of bankruptcy has been extensively studied in the accounting and finance field. It can have an important impact on lending decisions and the profitability of financial institutions in terms of risk management. Many researchers have focused on constructing a more robust bankruptcy prediction model. Early studies primarily used statistical techniques such as multiple discriminant analysis (MDA) and logit analysis for bankruptcy prediction. However, many studies have demonstrated that artificial intelligence (AI) approaches, such as artificial neural networks (ANN), decision trees, case-based reasoning (CBR), and support vector machine (SVM), have been outperforming statistical techniques since 1990s for business classification problems because statistical methods have some rigid assumptions in their application. In previous studies on corporate bankruptcy, many researchers have focused on developing a bankruptcy prediction model using financial ratios. However, there are few studies that suggest the specific types of bankruptcy. Previous bankruptcy prediction models have generally been interested in predicting whether or not firms will become bankrupt. Most of the studies on bankruptcy types have focused on reviewing the previous literature or performing a case study. Thus, this study develops a model using data mining techniques for predicting the specific types of bankruptcy as well as the occurrence of bankruptcy in Korean small- and medium-sized construction firms in terms of profitability, stability, and activity index. Thus, firms will be able to prevent it from occurring in advance. We propose a hybrid approach using two artificial neural networks (ANNs) for the prediction of bankruptcy types. The first is a back-propagation neural network (BPN) model using supervised learning for bankruptcy prediction and the second is a self-organizing map (SOM) model using unsupervised learning to classify bankruptcy data into several types. Based on the constructed model, we predict the bankruptcy of companies by applying the BPN model to a validation set that was not utilized in the development of the model. This allows for identifying the specific types of bankruptcy by using bankruptcy data predicted by the BPN model. We calculated the average of selected input variables through statistical test for each cluster to interpret characteristics of the derived clusters in the SOM model. Each cluster represents bankruptcy type classified through data of bankruptcy firms, and input variables indicate financial ratios in interpreting the meaning of each cluster. The experimental result shows that each of five bankruptcy types has different characteristics according to financial ratios. Type 1 (severe bankruptcy) has inferior financial statements except for EBITDA (earnings before interest, taxes, depreciation, and amortization) to sales based on the clustering results. Type 2 (lack of stability) has a low quick ratio, low stockholder's equity to total assets, and high total borrowings to total assets. Type 3 (lack of activity) has a slightly low total asset turnover and fixed asset turnover. Type 4 (lack of profitability) has low retained earnings to total assets and EBITDA to sales which represent the indices of profitability. Type 5 (recoverable bankruptcy) includes firms that have a relatively good financial condition as compared to other bankruptcy types even though they are bankrupt. Based on the findings, researchers and practitioners engaged in the credit evaluation field can obtain more useful information about the types of corporate bankruptcy. In this paper, we utilized the financial ratios of firms to classify bankruptcy types. It is important to select the input variables that correctly predict bankruptcy and meaningfully classify the type of bankruptcy. In a further study, we will include non-financial factors such as size, industry, and age of the firms. Thus, we can obtain realistic clustering results for bankruptcy types by combining qualitative factors and reflecting the domain knowledge of experts.

• Korean Business Review
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• v.20 no.2
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• pp.105-118
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• 2007

Recommender system is the tool for E-commerce company based on the internet for increasing their sales ratio in the market. Recommender system suggests the list of items which night be wanted by customers. This list generated by the result of customers' preference prediction through the prediction algorithm automatically. Recommender system will be able to offer not only the important information for marketing strategy but also reduce the cost of customers' information retrieval trough the analysis of customers' purchase patterns and features. But there are several problems like as the extension of the users and items scales and if the recommendation to customers generated by unreliable recommender system makes the customer royalty to the system to weaken. In this study, we propose the criterion for pre-evaluation on the prediction performance only using the preference ratings on the items which are rated by customers before prediction process and we study the features of customers who are classified through this classification criterion.

• KIPS Transactions on Computer and Communication Systems
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• v.8 no.4
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• pp.79-86
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• 2019

This paper describes a time-series data prediction based on artificial neural networks (ANN). In this study, a batch based ANN model and a stochastic ANN model have been implemented using TensorFlow libraries. Each model are evaluated by comparing training and testing errors that are measured through experiment. To train and test each model, tax dataset was used that are collected from the government website of indiana state budget agency in USA from 2001 to 2018. The dataset includes tax incomes of individual, product sales, company, and total tax incomes. The experimental results show that batch model reveals better performance than stochastic model. Using the batch scheme, we have conducted a prediction experiment. In the experiment, total taxes are predicted during next seven months, and compared with actual collected total taxes. The results shows that predicted data are almost same with the actual data.

• Journal of Industrial Convergence
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• v.20 no.4
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• pp.31-37
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• 2022

Research on what factors affect the success of the movie market is very important for reducing risks in related industries and developing the movie industry. In this study, in order to find out the degree of correlation of independent variables that affect movie performance, a survey was conducted on film experts using the AHP method and the importance of each measurement factor was evaluated. In addition, we hypothesized that factors derived from big data related to search portals and SNS will affect the success of movies due to the increase in the spread and use of smart phones. And a prediction model that reflects both the expert survey information and big data mentioned above was proposed. In order to check the accuracy of the prediction of the proposed model, it was confirmed that it was improved (10.5%) compared to the existing model as a result of verification with real data.Therefore, it is judged that the proposed model will be helpful in decision-making of film production companies and distributors.

• The Journal of the Convergence on Culture Technology
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• v.9 no.2
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• pp.533-543
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• 2023

This study explores the influence of the COVID-19 pandemic on the Korean art market and contrasts the classic hedonic method of art price prediction with the Artificial Neural Network technique. The empirical analysis of this paper utilizes 14,639 observations of Korean art auction data from 2015 to 2021. There are three types of variables in this study: artist-related, artwork-related, and sales-related. Previous studies have suggested that these three types of variables influence art prices. The empirical findings in this research are in twofold. First, in terms of RMSE and R², the Artificial Neural Network outperforms the hedonic model. Both techniques discover that sales and artwork variables have a greater impact than artist-related attributes. Second, when the primary factors of art price are controlled, Korean art prices are found to fall dramatically in 2020, shortly following the onset of COVID-19, but to rebound in 2021. The main lesson in this study is that the Artificial Neural Network enhances art price prediction and reduces information asymmetry in the Korean art market even in the face of unanticipated turmoil such as the COVID-19 outbreak.

• Journal of Korea Society of Digital Industry and Information Management
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• v.11 no.3
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• pp.219-230
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• 2015

It is difficult to make plans about the production schedule and volume of seasonal products due to the huge uncertainty in the prediction of their demands, which is why the amounts of carryover seasonal products increase after the peak season. Traditional models fail to meet the important requirements of production and stock plans related to the enhanced efficiency of logistics system due to the reduced value of carryover products by the disposal based on large discounts and deterioration, which poses considerable difficulties with actual problem solving. This study examined the stages of product storage from the specialized factory warehouses during a low season through the stores and the warehouses of local distribution centers during a high season to stock disposal and carryover product warehouses after a high season. The study developed a model for logistics rationalization plans to minimize carryover products by advance selling new products by subscription during a low season in anticipation of high season demands, increasing the accuracy of demands prediction, and making stable production plans, as well as demonstrated its excellence through numerical analysis.